Since its conception in 2006, differential privacy has emerged as the de-facto standard in
data privacy, owing to its robust mathematical guarantees, generalised applicability and rich …

Differential Privacy (DP) has received increasing attention as a rigorous privacy framework.
Many existing studies employ traditional DP mechanisms (eg, the Laplace mechanism) as …

Differential privacy (DP) is currently the de-facto standard for achieving privacy in data
analysis, which is typically implemented either in the" central" or" local" model. The local …

Differential privacy has emerged as an important standard for privacy preserving
computation over databases containing sensitive information about individuals. Research …

Data privacy has been an important research topic in the security, theory and database
communities in the last few decades. However, many existing studies have restrictive …

In differential privacy (DP), a challenging problem is to generate synthetic datasets that
efficiently capture the useful information in the private data. The synthetic dataset enables …

Sequential data is being increasingly used in a variety of applications. Publishing sequential
data is of vital importance to the advancement of these applications. However, as shown by …

Privacy-preserving data publishing is an important problem that has been the focus of
extensive study. The state-of-the-art solution for this problem is differential privacy, which …

Differential privacy is a theoretical framework for ensuring the privacy of individual-level data
when performing statistical analysis of privacy-sensitive datasets. This tutorial provides an …

LDP (Local Differential Privacy) has been widely studied to estimate statistics of personal
data (eg, distribution underlying the data) while protecting users' privacy. Although LDP …